Intro to R_D6
Set RNotebook for Root Directory
Set Working Directory
setwd("~/Desktop/af-werx")Getting working Directory
getwd()## [1] "/Users/datasociety/Desktop/af-werx/data"creating and setting the working directory
main_dir = "~/Desktop/af-werx"
data_dir = paste0(main_dir, "/data")
data_dir## [1] "~/Desktop/af-werx/data"plot_dir = paste0(main_dir, "/plots")
plot_dir## [1] "~/Desktop/af-werx/plots"setwd(data_dir)
getwd()## [1] "/Users/datasociety/Desktop/af-werx/data"COPYRIGHT - DATA SOCIETY
2 INTERACTIVE VISUALIZATION R
NOTE: To run individual pieces of code, select the line of code and
press ctrl + enter for PCs or command + enter for Macs
#=================================================- #### Slide 7: Load tidyverse and set up ggplot2 theme ####
# Load tidyverse library
# (it includes ggplot2).
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.2.0 ✔ purrr 0.3.2
## ✔ tibble 2.1.3 ✔ dplyr 0.8.3
## ✔ tidyr 0.8.3 ✔ stringr 1.4.0
## ✔ readr 1.3.1 ✔ forcats 0.4.0## ── Conflicts ────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()# Save our custom `ggplot` theme to a variable.
my_ggtheme = theme_bw() +
theme(axis.title = element_text(size = 20),
axis.text = element_text(size = 16),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
plot.title = element_text(size = 25),
plot.subtitle = element_text(size = 18)) #=================================================- #### Slide 8: Setup: load dataset for EDA ####
# Set working directory to where we store data.
#setwd(data_dir)
# Read CSV file called
#"ChemicalManufacturingProcess.csv".
CMP =
read.csv("ChemicalManufacturingProcess.csv",
header = TRUE)
# View CMP dataset in the tabular data explorer.
#View(CMP)#=================================================- #### Slide 10: Setup: subset data with select ####
# Select variables from CMP data.
CMP_subset = select(CMP, #<- set data
Yield:BiologicalMaterial03, #<- range of columns to select
ManufacturingProcess01:ManufacturingProcess03) #<- range of columns to select
# Inspect the first few observations in the subset.
head(CMP_subset)#=================================================- #### Slide 11: Setup: wide to long data conversion with gather ####
CMP_subset_long = CMP_subset %>%
gather(key = "variable",
value = "value")
# Inspect the first few observations.
head(CMP_subset_long)# Inspect the last few observations.
tail(CMP_subset_long)#=================================================- #### Slide 12: Setup: data cleaning with mutate ####
# Make names of processes and materials
# more user friendly and readable.
CMP_subset_long = CMP_subset_long %>%
# Replace `Biological` with `Bio`.
mutate(variable =
str_replace(variable, #<- in column `variable`
"Biological", #<- replace "Biological"
"Bio ")) %>% #<- with "Bio "
# Replace `Manufacturing` with `Man.`.
mutate(variable =
str_replace(variable,
"Manufacturing",
"Man. ")) %>%
# Remove `0` from numbering.
mutate(variable =
str_replace(variable,
"0",
" "))
# Inspect few first
# entries in the data.
head(CMP_subset_long)# Inspect few last
# entries in the data.
tail(CMP_subset_long)#=================================================- #### Slide 16: Set up & link data: make boxplots ####
# A basic boxplot with pre-saved theme.
boxplots = ggplot(CMP_subset_long, #<- set the base plot + data
aes(x = variable, #<- map `variable` to x-axis
y = value)) + #<- map `value` to y-axis
geom_boxplot() + #<- add boxplot geom
my_ggtheme #<- add pre-saved theme
# View plot.
boxplots## Warning: Removed 19 rows containing non-finite values (stat_boxplot).
#=================================================- #### Slide 17: Adjust: boxplot aesthetics ####
# Make color of fill based on variable.
boxplots = ggplot(CMP_subset_long,
aes(x = variable,
y = value,
fill = variable)) + #<- add fill to aesthetics
geom_boxplot() +
my_ggtheme
# View plot.
boxplots## Warning: Removed 19 rows containing non-finite values (stat_boxplot).
#=================================================- #### Slide 18: Adjust & polish: boxplot legends ####
# Remove redundant legend.
boxplots = boxplots +
labs(title = "CMP data variables", #<- add title and subtitle
subtitle = "Boxplot of unscaled data") +
guides(fill = FALSE) #<- remove fill legend
# View plot.
boxplots## Warning: Removed 19 rows containing non-finite values (stat_boxplot).
#=================================================- #### Slide 20: Setup: normalize data with group_by + mutate ####
# Normalize the CMP data.
CMP_subset_long = CMP_subset_long %>%
group_by(variable) %>% #<- group values by variable
mutate(norm_value = #<- make `norm_value` column
value/max(value, #<- divide value by group max
na.rm = TRUE)) #<- don't forget the NAs!
CMP_subset_long#=================================================- #### Slide 21: Setup: make base plot with normalized data ####
# Construct the base plot for normalized data.
base_norm_plot = ggplot(CMP_subset_long, #<- set data
aes(x = variable, #<- map `variable`
y = norm_value, #<- map `norm_value`
fill = variable)) + #<- set fill
my_ggtheme #<- add theme
# View base plot + theme.
base_norm_plot
#=================================================- #### Slide 22: Adjust: normalized boxplot’s effects & legends ####
# Make color of fill based on variable.
boxplots_norm = base_norm_plot + #<- set base plot
geom_boxplot() + #<- add geom
guides(fill = FALSE) #<- remove redundant legend
# View updated plot.
boxplots_norm## Warning: Removed 19 rows containing non-finite values (stat_boxplot).
#=================================================- #### Slide 23: Polish: normalized boxplot details ####
# Make outliers stand out with red color and bigger size.
boxplots_norm = boxplots_norm + #<- previously saved plot
geom_boxplot(outlier.color = "red", #<- adjust outlier color
outlier.size = 5) + #<- adjust outlier size
labs(title = "CMP data variables", #<- add title and subtitle
subtitle = "Boxplot of scaled data")
# View updated plot.
boxplots_norm## Warning: Removed 19 rows containing non-finite values (stat_boxplot).
## Warning: Removed 19 rows containing non-finite values (stat_boxplot).
#=================================================- #### Slide 25: Setup the density plot of normalized data ####
# Let's save base plot for density as well.
density_norm = ggplot(CMP_subset_long, #<- set data
aes(x = norm_value, #<- map
fill = variable)) + #<- map fill
my_ggtheme + #<- add theme
geom_density(alpha = 0.3) #<- adjust fill
density_norm## Warning: Removed 19 rows containing non-finite values (stat_density).
#=================================================- #### Slide 26: Adjust the axes and visual effects of density plot ####
# Instead of overlaying densities, split them into individual plots called facets.
density_norm = density_norm + #<- previously saved plot
facet_wrap (~ variable, #<- make facets by variable
ncol = 4) + #<- set a 4-column grid
geom_vline(data = CMP_subset_long, #<- set data
aes(xintercept = #<- set x-intercept
mean(norm_value, #<- to mean
na.rm = TRUE), #<- handle NAs!
color = variable), #<- map color
linetype = "dashed", #<- set line type
size = 1.5) #<- set line size
# View updated plot.
density_norm## Warning: Removed 19 rows containing non-finite values (stat_density).
#=================================================- #### Slide 27: Polish: legends of density plot ####
# Remove redundant legend.
density_norm = density_norm + #<- previously saved plot
guides(fill = FALSE, #<- no legend for `fill` of density
color = FALSE) + #<- no legend for `color` of line
theme(strip.text.x =
element_text(size = 14)) + #<- set strip text size
labs(title = "CMP data variables", #<- add title and subtitle
subtitle = "Density of scaled data")
density_norm## Warning: Removed 19 rows containing non-finite values (stat_density).
#=================================================- #### Slide 29: Exercise 1 ####
still need to do
#=================================================- #### Slide 33: Setup: transform data for scatterplot ####
CMP_subset_long2 = CMP_subset %>%
gather(BiologicalMaterial01:ManufacturingProcess03, #<- gather all variables but `Yield`
key = "variable", #<- set key to `variable`
value = "value") %>% #<- set value to `value`
# All other transformations we've done before.
mutate(variable = str_replace(variable, "Biological", "Bio ")) %>%
mutate(variable = str_replace(variable, "Manufacturing", "Man. ")) %>%
mutate(variable = str_replace(variable, "0", " ")) %>%
group_by(variable) %>%
mutate(norm_value = value/max(value, na.rm = TRUE))
# Inspect the data.
head(CMP_subset_long2)#=================================================- #### Slide 34: Setup & link: normalized data base plot ####
# Create a base plot.
base_norm_plot = ggplot(data = CMP_subset_long2, #<- set data
aes(x = norm_value, #<- set x-axis to represent normalized value
y = Yield, #<- y-axis to represent `Yield`
color = variable)) + #<- set color to depend on `variable`
my_ggtheme #<- set theme#=================================================- #### Slide 35: Setup & adjust: normalized data scatterplot ####
# Create a scatterplot.
scatter_norm = base_norm_plot + #<- base plot
geom_point(size = 3, #<- add point geom with size of point = 3
alpha = 0.7) #<- make it 70% opaque
# View updated plot.
scatter_norm## Warning: Removed 19 rows containing missing values (geom_point).
#=================================================- #### Slide 36: Adjust: add density geom to scatterplot ####
# Adjust scatterplot to include 2D density.
scatter_norm = scatter_norm + #<- previously saved plot
geom_density2d(alpha = 0.7) #<- add 2D density geom with 70% opaque color
# View updated plot.
scatter_norm## Warning: Removed 19 rows containing non-finite values (stat_density2d).## Warning: Removed 19 rows containing missing values (geom_point).
#=================================================- #### Slide 37: Adjust: wrap scatterplots in facets ####
# Wrap each scatterplot into a facet.
scatter_norm = scatter_norm + #<- previously saved plot
facet_wrap(~ variable, ncol = 3) #<- wrap plots by variable into 3 columns
# View updated plot.
scatter_norm## Warning: Removed 19 rows containing non-finite values (stat_density2d).## Warning: Removed 19 rows containing missing values (geom_point).
#=================================================- #### Slide 38: Adjust & polish: legends and text in scatterplot ####
# Add finishing touches to the plot.
scatter_norm = scatter_norm + #<- previously saved plot
guides(color = FALSE) + #<- remove legend for color mappings
theme(strip.text.x = element_text(size = 14)) + #<- increase text size in strips of facets
labs(title = "CMP data: Yield vs. other variables",#<- add title and subtitle
subtitle = "2D distribution of scaled data")
# View updated plot.
scatter_norm## Warning: Removed 19 rows containing non-finite values (stat_density2d).## Warning: Removed 19 rows containing missing values (geom_point).
#=================================================- #### Slide 40: Saving plots in R: PNG exported ####
Set working directory
to where we want to save our plots.
setwd(plot_dir)
png("CMP_boxplots_norm.png",
width = 1200,
height = 600,
units = "px")
boxplots_norm## Warning: Removed 19 rows containing non-finite values (stat_boxplot).
## Warning: Removed 19 rows containing non-finite values (stat_boxplot).dev.off()## quartz_off_screen
## 2png("CMP_density_norm.png",
width = 1200,
height = 600,
units = "px")
density_norm## Warning: Removed 19 rows containing non-finite values (stat_density).dev.off()## quartz_off_screen
## 2png("CMP_scatterplot_norm.png",
width = 1200,
height = 600,
units = "px")
scatter_norm## Warning: Removed 19 rows containing non-finite values (stat_density2d).## Warning: Removed 19 rows containing missing values (geom_point).dev.off()## quartz_off_screen
## 2#=================================================- #### Slide 43: Saving plots in R: PDF exported ####
# Set working directory to where you want to save plots.
setwd(plot_dir)
pdf("CMP_plots.pdf", #<- name of file
width = 16, #<- width in inches
height = 8) #<- height ...
boxplots_norm #<- plot 1## Warning: Removed 19 rows containing non-finite values (stat_boxplot).
## Warning: Removed 19 rows containing non-finite values (stat_boxplot).density_norm #<- plot 2## Warning: Removed 19 rows containing non-finite values (stat_density).scatter_norm #<- plot 3## Warning: Removed 19 rows containing non-finite values (stat_density2d).## Warning: Removed 19 rows containing missing values (geom_point).dev.off() #<- clear graphics device## quartz_off_screen
## 2#=================================================- #### Slide 45: Exercise 2 ####
#=================================================- #### Slide 47: Interactive visualizations: highcharter ####
# Install `highcharter` package.
#install.packages("highcharter")
# Load the library.
library(highcharter)## Registered S3 method overwritten by 'xts':
## method from
## as.zoo.xts zoo## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## Highcharts (www.highcharts.com) is a Highsoft software product which is## not free for commercial and Governmental use# View documentation.
library(help = "highcharter")#=================================================- #### Slide 55: Highcharts generic function hchart: scatter ####
# Construct an interactive scatterplot.
scatter_interactive = #<- name the plot
hchart(CMP_subset_long2, #<- set data
"scatter", #<- make type "scatter"
hcaes(x = norm_value, #<- map x-axis
y = Yield, #<- map y-axis
group = variable)) #<- group by## Warning: `parse_quosure()` is deprecated as of rlang 0.2.0.
## Please use `parse_quo()` instead.
## This warning is displayed once per session.#=================================================- #### Slide 56: Highcharts generic function hchart: scatter ####
scatter_interactive#=================================================- #### Slide 58: Highcharts generic function hchart: scatter ####
# Pipe chart options to original chart.
scatter_interactive = scatter_interactive %>%
# Use chart options to specify zoom.
hc_chart(zoomType = "xy")
scatter_interactive#=================================================- #### Slide 59: Highcharts generic function hchart: scatter ####
# Pipe chart options to original chart.
scatter_interactive = scatter_interactive %>%
# Add title to your plot.
hc_title(text = "CMP data: Yield vs. other variables")
scatter_interactive#=================================================- #### Slide 60: Correlation matrix with hchart ####
# Compute a correlation matrix for the first
# 4 variables in our data.
cor_matrix = cor(CMP_subset[, 1:4])# Construct a correlation plot by
# simply giving the plotting function
# a correlation matrix.
correlation_interactive = hchart(cor_matrix) %>%
# Add title to your plot.
hc_title(text = "CMP data: correlation")
correlation_interactive#=================================================- #### Slide 62: Exercise 3 ####
#=================================================- #### Slide 65: Summary column plot with hchart ####
# Create data summary.
CMP_summary = summary(CMP_subset)
# Save it as a dataframe.
CMP_summary = as.data.frame(CMP_summary)
# Inspect the data.
head(CMP_summary)# Remove an empty variable.
CMP_summary$Var1 = NULL
# Rename remaining columns.
colnames(CMP_summary) = c("Variable",
"Summary")
# Inspect updated data.
head(CMP_summary)#=================================================- #### Slide 66: Summary column plot with hchart ####
# Separate `Summary` column into 2 columns.
CMP_summary = CMP_summary %>% #<- set original data
separate(Summary, #<- separate `Summary` variable
into = c("Statistic", "Value"), #<- into 2 columns: `Statistic`, `Value`
sep = ":", #<- set separating character
convert = TRUE) #<- where applicable convert data (to numeric)# Inspect the first few entries in the data.
head(CMP_summary)# Inspect total number of rows in data including NAs.
nrow(CMP_summary)## [1] 49#=================================================- #### Slide 67: Summary column plot with hchart ####
# Inspect `Value` column for `NAs`.
which(is.na(CMP_summary$Value) == TRUE)## [1] 7 14 21 28# Subset only rows where `Value` is not NAs.
CMP_summary = subset(CMP_summary, !is.na(Value))# Now the number of rows should be 4 less.
nrow(CMP_summary)## [1] 45# Construct the summary chart.
CMP_summary_interactive =
hchart(CMP_summary, #<- set data
"column", #<- set type (`column` in highcharts)
hcaes(x = Statistic, #<- arrange `Statistics` across x-axis
y = Value, #<- map `Value` of each `Statistic` to y-axis
group = Variable)) #<- group columns by `Variable`#=================================================- #### Slide 68: Summary column plot with hchart ####
CMP_summary_interactive#=================================================- #### Slide 69: Summary column plot with hchart ####
# Adjust tooltip options by piping `hc_tooltip` to base plot.
CMP_summary_interactive = CMP_summary_interactive %>%
hc_tooltip(shared = TRUE) %>% #<- `shared` needs to be set to `TRUE`
hc_title(text = "CMP data variable summary") #<- add title to your plot#=================================================- #### Slide 70: Summary column plot with hchart ####
CMP_summary_interactive#=================================================- #### Slide 73: Highcharts boxplot: hcboxplot ####
# Construct an interactive boxplot.
CMP_subset_longboxplot_interactive =
hcboxplot(x = CMP_subset_long$norm_value,
var = CMP_subset_long$variable,
name = "Normalized value") %>%
hc_title(text = "CMP data variables")
boxplot_interactive#=================================================- #### Slide 76: Highcharts boxplot: hcboxplot ####
# Enhance original boxplot with some options.
boxplot_interactive = boxplot_interactive %>%
hc_plotOptions( #<- plot options
boxplot = list( #<- for boxplot
colorByPoint = TRUE)) #<- color each box
boxplot_interactive#=================================================- #### Slide 79: Compound plots: density + lines example ####
layered_density_interactive = highchart() %>% #main chart
hc_chart(type = "area") %>% #global chart option for to apply for all layers
hc_add_series(data = density(CMP_subset$Yield),
name = "Yield") %>%
hc_add_series(data = density(CMP_subset$BiologicalMaterial01),
name = "Bio Material 1") %>%
hc_add_series(data = density(CMP_subset$ManufacturingProcess01, na.rm = TRUE),
name = "Man. Process 1") %>%
hc_xAxis(plotLines = list(
list(label = list(text = "Yield avg."),
width = 2,
color = "red",
value = mean(CMP_subset$Yield)),
list(label = list(text = "Bio Material 1 avg."),
width = 2,
color = "red",
value = mean(CMP_subset$BiologicalMaterial01)),
list(label = list(text = "Man. Process 1 avg."),
width = 2,
color = "red",
value = mean(CMP_subset$ManufacturingProcess01, na.rm = TRUE)))) %>%
hc_tooltip(crosshairs = TRUE) %>%
hc_title(text = "CMP data: density and average of select variables")#=================================================- #### Slide 80: Compound plots: highchart with layers ####
layered_density_interactive#=================================================- #### Slide 83: Exercise 4 ####